导电添加剂制造的丙烯腈-丁二烯-苯乙烯长丝:机械和电气行为的统计方法

IF 2.3 4区 工程技术 Q3 ENGINEERING, MANUFACTURING
3D Printing and Additive Manufacturing Pub Date : 2023-12-01 Epub Date: 2023-12-11 DOI:10.1089/3dp.2022.0287
Osman Ulkir
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引用次数: 0

摘要

增材制造是一种利用数字化三维(3D)设计数据,通过累积材料分层制造部件的工艺。增材制造技术使用的材料有很多。区分这些材料的最基本特征是其强度和电气性能。根据应用的不同,这些材料可以是坚固的,也可以是柔韧的、耐磨的。最近,3D 打印长丝和聚合物复合材料与具有导电性的碳纳米结构相结合得到了应用。在本研究中,丙烯腈-丁二烯-苯乙烯(ABS)是一种填充碳黑的导电材料,具有高强度和高硬度。本研究的目的是重点研究以长丝形式加工的材料的机械和电气行为。样品的制作是通过基于熔融沉积模型的打印机完成的,该打印机可控制长丝的方向。进行了不同的实验研究:(1)机械测试,以确定样品的最大拉伸强度值;(2)电气测试,以分析样品的电阻。在第一个实验的设计中,确定了填充量、层高、填充类型和印刷方向作为影响强度的因素。在第二个实验的设计中,长度、喷嘴温度和测量温度被确定为影响电阻的因素。对测量数据进行了统计分析,以评估实验的总体结果。最后,使用机器学习算法创建了实时拉伸强度和电阻值的预测模型。这些算法是高斯过程回归和支持向量机。实验结果证实了电阻与 3D 打印导电 ABS 样品长度的已知线性关系,并显示了改变制造设置对强度值的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviors.

Additive manufacturing is a process in which digital three-dimensional (3D) design data are used to build a component in layers by accumulating materials. There are many materials used in additive manufacturing technology. The most basic features that distinguish these materials are their strength and electrical behavior. They can be strong or flexible, resistant to abrasion, depending on the application used. Recently, 3D printing filament and polymeric composite materials combined with carbon nanostructures with electrical conductivity have been used. In this study, acrylonitrile butadiene styrene (ABS), a carbon black-filled conductive material with high strength and hardness, was preferred. The aim in this study is to focus on the mechanical and electrical behavior of the material processed in filament form. Fabrication of samples was done using a fused deposition modeling-based printer that controls filament orientation. Different experimental studies were conducted: (1) mechanical tests to determine the maximum tensile strength values of the samples; and (2) electrical tests to analyze the electrical resistances of the samples. In the design of the first experiment, infill volume, layer height, infill type, and printing direction were determined as factors affecting strength. In the design of the second experiment, the length, nozzle temperature, and measurement temperature were determined as the factors affecting the electrical resistance. Statistical analysis of the measured data was performed to evaluate the overall result of the experiments. Finally, a prediction model of real-time tensile strength and resistance values was created using machine learning algorithms. These algorithms are Gaussian Process Regression and Support Vector Machine. The results confirmed the known linear dependence of electrical resistance on the length of the 3D-printed conductive ABS samples and showed how changing the fabrication settings affected the strength values.

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来源期刊
3D Printing and Additive Manufacturing
3D Printing and Additive Manufacturing Materials Science-Materials Science (miscellaneous)
CiteScore
6.00
自引率
6.50%
发文量
126
期刊介绍: 3D Printing and Additive Manufacturing is a peer-reviewed journal that provides a forum for world-class research in additive manufacturing and related technologies. The Journal explores emerging challenges and opportunities ranging from new developments of processes and materials, to new simulation and design tools, and informative applications and case studies. Novel applications in new areas, such as medicine, education, bio-printing, food printing, art and architecture, are also encouraged. The Journal addresses the important questions surrounding this powerful and growing field, including issues in policy and law, intellectual property, data standards, safety and liability, environmental impact, social, economic, and humanitarian implications, and emerging business models at the industrial and consumer scales.
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